Generating an object time series from data objects

ABSTRACT

Systems and methods are presented for representing non-numerical data objects in an object time series. An object time series of can be created by establishing one or more associations, each association including a mapping of at least one point in time with one or more objects that include properties and values. Visual representation of an object time series may include displaying non-numerical values associated with objects in the object time series in association with respective points in time.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.61/799,691, entitled “OBJECT TIME SERIES,” which was filed Mar. 15, 2013and is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to systems and techniques for dataintegration, analysis, and visualization. More specifically,representing non-numerical data objects in a time series.

BACKGROUND

Traditionally, time series are created and used to store numerical dataassociated with a plurality of points in time. Non-numerical data areusually stored and used as metadata.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates one embodiment of a visualization of an object timeseries that may be generated and presented to a user.

FIG. 2 illustrates one embodiment of a database system using anontology.

FIG. 3 illustrates one embodiment of a system for creating data in adata store using a dynamic ontology.

FIG. 4 illustrates a sample user interface using relationships describedin a data store using a dynamic ontology.

FIG. 5 illustrates an example figure that may be generated and presentedto a user to visualize a traditional time series with associatingnumerical values.

FIG. 6 illustrates an example figure that may be generated and presentedto a user to visualize an Object Time Series associated with a specificinstance of a payment event object.

FIG. 7 illustrates an example figure that may be generated and presentedto a user to visualize an Object Time Series and the slicing of theObject Time Series.

FIG. 8 illustrates a computer system with which certain methodsdiscussed herein may be implemented.

FIG. 9 illustrates an example figure that may be generated and presentedto a user to visualize an Object Time Series associated with multipletypes of object and functions that may be used to access property valuesof the person object.

FIG. 9A illustrates an example figure that may be generated andpresented to a user to visualize two Object Time Series associated withstatus objects to show duration.

FIG. 9B illustrates an example figure that may be generated andpresented to a user to visualize two Object Time Series associated withstatus objects indicating progress over time.

FIG. 9C illustrates an example figure that may be generated andpresented to a user to visualize two Object Time Series associated withevents.

FIG. 9D illustrates an example figure that may be generated andpresented to a user to visualize an Object Time Series associated with astatus object.

FIG. 9E illustrates an example figure that may be generated andpresented to a user to visualize an Object Time Series associated with astatus object and transitions among the various statuses.

FIG. 10 illustrates a computer system with which certain methodsdiscussed herein may be implemented.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS Definitions

In order to facilitate an understanding of the systems and methodsdiscussed herein, a number of terms are defined below. The terms definedbelow, as well as other terms used herein, should be construed toinclude the provided definitions, the ordinary and customary meaning ofthe terms, and/or any other implied meaning for the respective terms.Thus, the definitions below do not limit the meaning of these terms, butonly provide exemplary definitions.

Ontology: Stored information that provides a data model for storage ofdata in one or more databases. For example, the stored data may comprisedefinitions for object types and property types for data in a database,and how objects and properties may be related.

Database: A broad term for any data structure for storing and/ororganizing data, including, but not limited to, relational databases(Oracle database, mySQL database, etc.), spreadsheets, XML files, andtext file, among others.

Data Object or Object: A data container for information representingspecific things in the world that have a number of definable properties.For example, a data object can represent an entity such as a person, aplace, an organization, a market instrument, or other noun. A dataobject can represent an event that happens at a point in time or for aduration. A data object can represent a document or other unstructureddata source such as an e-mail message, a news report, or a written paperor article. Each data object may be associated with a unique identifierthat uniquely identifies the data object. The object's attributes (e.g.metadata about the object) may be represented in one or more properties.

Object Type: Type of a data object (e.g., Person, Event, or Document).Object types may be defined by an ontology and may be modified orupdated to include additional object types. An object definition (e.g.,in an ontology) may include how the object is related to other objects,such as being a sub-object type of another object type (e.g. an agentmay be a sub-object type of a person object type), and the propertiesthe object type may have.

Properties: Attributes of a data object that represent individual dataitems. At a minimum, each property of a data object has a property typeand a value or values.

Property Type: The type of data a property is, such as a string, aninteger, or a double. Property types may include complex property types,such as a series data values associated with timed ticks (e.g. a timeseries), etc.

Property Value: The value associated with a property, which is of thetype indicated in the property type associated with the property. Aproperty may have multiple values.

Link: A connection between two data objects, based on, for example, arelationship, an event, and/or matching properties. Links may bedirectional, such as one representing a payment from person A to B, orbidirectional.

Link Set: Set of multiple links that are shared between two or more dataobjects.

Object Time Series: associating multiple points in time with one or moreobjects, for example, People, Event, and so forth. Objects included inObject Time Series may be any type of objects, including numericalobjects and non-numerical objects.

Overview of Object Time Series

Traditional time series may store data in the format of TIME, VALUE,otherwise known as a tick. For example, the stock price of Microsoft(“MSFT”) may be represented by a specific time of any given businessday, and a value of the stock at that specific time. However, usingtraditional time series data types to store generic values that are notnumerical may be very difficult because data about objects 201 and aboutspecific object types such as person, event, document, and so forth,when associated with a time series, have been traditionally implementedas metadata associated with values in time series.

For example, in the MSFT stock price use case, specific milestones suchas quarterly earnings reports, board meetings, and dividends may beassociated with a specific value of the MSFT stock price in a timeseries. However, such milestones (event) data may not be easilyrepresented because traditional time series treat the milestones asmetadata (e.g. not objects in themselves, but as descriptive text dataassociated with a numeric time series). Accessing such metadata mayrequire several steps of referencing and/or mapping. Slicing andsampling traditional time series data based on metadata is alsoinefficient and difficult. Details regarding traditional time series arediscussed further below.

An Object Time Series may instead directly associate time with anyobject. For example, instead of limiting the value associated with apoint in time to numerical values, any object type may be used, such asPerson, Event, and so forth.

FIG. 1 illustrates one embodiment of a visualization of an object timeseries that may be generated and presented to a user. In this example,an Object Time Series 100 is plotted. The Object Time Series 100 spansfrom Jan. 1, 2013 to Apr. 1, 2013. The data object associated with eachdate in the Object Time Series 100 is a Contagious Disease Reportobject. As a data object, the Contagious Disease Report object maycontain properties such as: (1) type of contagious disease; (2)location; (3) State; (4) a patient object (age, gender, occupation); (5)hospital where the patient is or has been treated. As can be seen inthis example, the object associated with an Object Time Series mayitself contain other object types, which may involve more properties.Thus, Object Time Series may associate rich information and object typeswith time/dates.

In some embodiments, Object Time Series 100 data may be plotted similarto traditional time series, such as the example in FIG. 1. In some otherembodiments, data involved in object time series may be used to identifylinks and/or relationships. For example, if the Object Time Series 100is named a Disease OTS, then executing a function such asDiseaseOTS.location( ) may automatically generate a list of alllocations that a disease has been reported, which may be the followinglist: (Aliso Viejo, Miami, Mission Viejo). In another example, executinga function such as DiseaseOTS.occupation( ) may generate the followinglist automatically: (Coast Elementary, Rancho High School, Mission ViejoShopping Center, Sunny Restaurant). Moreover, in some embodiments, thelist of locations generated may be automatically mapped to therespective points in time. Advantageously, this would allow a disease tobe tracked by location in relation to time.

As shown in the example in FIG. 1, the objects stored in Object TimeSeries 100 may include not only numerical values, but also other datatypes involving multiple properties. For example, the hospitals inObject Time Series 100 may be associated property values such ashospital location, treating physician, and so forth.

In some embodiments, Object Time Series 100 may also enable fastvisualization and association between stored objects at various pointsin time. For example, because the occupation information may be directlyaccessible and visualized in Object Time Series 100, an analystreviewing the DiseaseOTS Object Time Series may easily find that WestNile Virus has been reported regarding two Coast Elementary Schoolchildren, reported on Jan. 1, 2013 and Feb. 9, 2013, respectively.Moreover, it may be easy to show that both Mission Viejo and Aliso Viejohave been reported to have West Nile occurrences twice. More detailsregarding Object Time Series are discussed further herein.

Object Centric Data Model

To provide a framework for the following discussion of specific systemsand methods described herein, an example database system 210 using anontology 205 will now be described. This description is provided for thepurpose of providing an example and is not intended to limit thetechniques to the example data model, the example database system, orthe example database system's use of an ontology to representinformation.

In one embodiment, a body of data is conceptually structured accordingto an object-centric data model represented by ontology 205. Theconceptual data model is independent of any particular database used fordurably storing one or more database(s) 209 based on the ontology 205.For example, each object of the conceptual data model may correspond toone or more rows in a relational database or an entry in LightweightDirectory Access Protocol (LDAP) database, or any combination of one ormore databases.

FIG. 2 illustrates an object-centric conceptual data model according toan embodiment. An ontology 205, as noted above, may include storedinformation providing a data model for storage of data in the database209. The ontology 205 may be defined by one or more object types, whichmay each be associated with one or more property types. At the highestlevel of abstraction, data object 201 is a container for informationrepresenting things in the world. For example, data object 201 canrepresent an entity such as a person, a place, an organization, a marketinstrument, or other noun. Data object 201 can represent an event thathappens at a point in time or for a duration. Data object 201 canrepresent a document or other unstructured data source such as an e-mailmessage, a news report, or a written paper or article. Each data object201 is associated with a unique identifier that uniquely identifies thedata object within the database system.

Different types of data objects may have different property types. Forexample, a “Person” data object might have an “Eye Color” property typeand an “Event” data object might have a “Date” property type. Eachproperty 203 as represented by data in the database system 210 may havea property type defined by the ontology 205 used by the database 205.

Objects may be instantiated in the database 209 in accordance with thecorresponding object definition for the particular object in theontology 205. For example, a specific monetary payment (e.g., an objectof type “event”) of US$30.00 (e.g., a property of type “currency”)taking place on Mar. 27, 2009 (e.g., a property of type “date”) may bestored in the database 209 as an event object with associated currencyand date properties as defined within the ontology 205.

The data objects defined in the ontology 205 may support propertymultiplicity. In particular, a data object 201 may be allowed to havemore than one property 203 of the same property type. For example, a“Person” data object might have multiple “Address” properties ormultiple “Name” properties.

Each link 202 represents a connection between two data objects 201. Inone embodiment, the connection is either through a relationship, anevent, or through matching properties. A relationship connection may beasymmetrical or symmetrical. For example, “Person” data object A may beconnected to “Person” data object B by a “Child Of” relationship (where“Person” data object B has an asymmetric “Parent Of” relationship to“Person” data object A), a “Kin Of” symmetric relationship to “Person”data object C, and an asymmetric “Member Of” relationship to“Organization” data object X. The type of relationship between two dataobjects may vary depending on the types of the data objects. Forexample, “Person” data object A may have an “Appears In” relationshipwith “Document” data object Y or have a “Participate In” relationshipwith “Event” data object E. As an example of an event connection, two“Person” data objects may be connected by an “Airline Flight” dataobject representing a particular airline flight if they traveledtogether on that flight, or by a “Meeting” data object representing aparticular meeting if they both attended that meeting. In oneembodiment, when two data objects are connected by an event, they arealso connected by relationships, in which each data object has aspecific relationship to the event, such as, for example, an “AppearsIn” relationship.

As an example of a matching properties connection, two “Person” dataobjects representing a brother and a sister, may both have an “Address”property that indicates where they live. If the brother and the sisterlive in the same home, then their “Address” properties likely containsimilar, if not identical property values. In one embodiment, a linkbetween two data objects may be established based on similar or matchingproperties (e.g., property types and/or property values) of the dataobjects. These are just some examples of the types of connections thatmay be represented by a link and other types of connections may berepresented; embodiments are not limited to any particular types ofconnections between data objects. For example, a document might containreferences to two different objects. For example, a document may containa reference to a payment (one object), and a person (a second object). Alink between these two objects may represent a connection between thesetwo entities through their co-occurrence within the same document.

Each data object 201 can have multiple links with another data object201 to form a link set 204. For example, two “Person” data objectsrepresenting a husband and a wife could be linked through a “Spouse Of”relationship, a matching “Address” property, and one or more matching“Event” properties (e.g., a wedding). Each link 202 as represented bydata in a database may have a link type defined by the database ontologyused by the database.

FIG. 3 is a block diagram illustrating exemplary components and datathat may be used in identifying and storing data according to anontology. In this example, the ontology may be configured, and data inthe data model populated, by a system of parsers and ontologyconfiguration tools. In the embodiment of FIG. 3, input data 300 isprovided to parser 302. The input data may comprise data from one ormore sources. For example, an institution may have one or more databaseswith information on credit card transactions, rental cars, and people.The databases may contain a variety of related information andattributes about each type of data, such as a “date” for a credit cardtransaction, an address for a person, and a date for when a rental caris rented. The parser 302 is able to read a variety of source input datatypes and determine which type of data it is reading.

In accordance with the discussion above, the example ontology 205comprises stored information providing the data model of data stored indatabase 209, and the ontology is defined by one or more object types310, one or more property types 316, and one or more link types 330.Based on information determined by the parser 302 or other mapping ofsource input information to object type, one or more data objects 201may be instantiated in the database 209 based on respective determinedobject types 310, and each of the objects 201 has one or more properties203 that are instantiated based on property types 316. Two data objects201 may be connected by one or more links 202 that may be instantiatedbased on link types 330. The property types 316 each may comprise one ormore data types 318, such as a string, number, etc. Property types 316may be instantiated based on a base property type 320. For example, abase property type 320 may be “Locations” and a property type 316 may be“Home.”

In an embodiment, a user of the system uses an object type editor 324 tocreate and/or modify the object types 310 and define attributes of theobject types. In an embodiment, a user of the system uses a propertytype editor 326 to create and/or modify the property types 316 anddefine attributes of the property types. In an embodiment, a user of thesystem uses link type editor 328 to create the link types 330.Alternatively, other programs, processes, or programmatic controls maybe used to create link types and property types and define attributes,and using editors is not required.

In an embodiment, creating a property type 316 using the property typeeditor 326 involves defining at least one parser definition using aparser editor 322. A parser definition comprises metadata that informsparser 302 how to parse input data 300 to determine whether values inthe input data can be assigned to the property type 316 that isassociated with the parser definition. In an embodiment, each parserdefinition may comprise a regular expression parser 304A or a codemodule parser 304B. In other embodiments, other kinds of parserdefinitions may be provided using scripts or other programmaticelements. Once defined, both a regular expression parser 304A and a codemodule parser 304B can provide input to parser 302 to control parsing ofinput data 300.

Using the data types defined in the ontology, input data 300 may beparsed by the parser 302 determine which object type 310 should receivedata from a record created from the input data, and which property types316 should be assigned to data from individual field values in the inputdata. Based on the object-property mapping 301, the parser 302 selectsone of the parser definitions that is associated with a property type inthe input data. The parser parses an input data field using the selectedparser definition, resulting in creating new or modified data 303. Thenew or modified data 303 is added to the database 209 according toontology 205 by storing values of the new or modified data in a propertyof the specified property type. As a result, input data 300 havingvarying format or syntax can be created in database 209. The ontology205 may be modified at any time using object type editor 324, propertytype editor 326, and link type editor 328, or under program controlwithout human use of an editor. Parser editor 322 enables creatingmultiple parser definitions that can successfully parse input data 300having varying format or syntax and determine which property typesshould be used to transform input data 300 into new or modified inputdata 303.

The properties, objects, and the links (e.g. relationships) between theobjects can be visualized using a graphical user interface (GUI). Forexample, FIG. 4 displays a user interface showing a graph representation403 of relationships (including relationships or links 404, 405, 406,407, 408) between the data objects (including data objects 411, 412,413, 414, 415, 416) that are represented as nodes in the example of FIG.4. In this embodiment, the data objects are person objects. In thisexample, the person nodes (associated with person data objects) may haverelationships to other person nodes, for example, through paymentobjects. For example, relationship 404 is based on a payment associatedwith the individuals indicated in person data objects 411 and 413. Thelink 404 represents these shared payments (for example, the individualassociated with data object 411 may have paid the individual associatedwith data object 413 on three occasions). These relationships may bestored as links, or in some embodiments, as properties, where arelationship may be detected between the properties. In some cases, asstated above, the links may be directional. For example, a payment linkmay have a direction associated with the payment, where one personobject is a receiver of a payment, and another person object is thepayer of payment.

In addition to visually showing relationships between the data objects,the user interface may allow various other manipulations. For example,the objects within database 108 may be searched using a search interface420 (e.g., text string matching of object properties), inspected (e.g.,properties and associated data viewed), filtered (e.g., narrowing theuniverse of objects into sets and subsets by properties orrelationships), and statistically aggregated (e.g., numericallysummarized based on summarization criteria), among other operations andvisualizations.

Object Time Series

FIG. 5 illustrates an example figure that may be generated and presentedto a user to visualize a traditional time series 500 with associatednumeric values. In time series 500, the time series bar represents fivemonths, beginning from Jan. 1, 2013 and ending on May 1, 2013. At thebeginning of each month, a numerical value is associated with the date.For example, the numerical value associated with Jan. 1, 2013 is$230,450. The numerical value associated with Feb. 1, 2013 is $231,400.The numerical value associated with Mar. 1, 2013 is $229,905. Thenumerical value associated with Apr. 1, 2013 is $230,090. The numericalvalue associated with May 1, 2013 is $232,500.

Traditional time series such as time series 500 stores a date/time and anumerical value. However, in order to find out other data associatedwith the numerical value, further steps may be needed. For example, insome embodiments, a traditional time series 500 may implement an arraythat stores (DATE, VALUE) pairs, for example:

$\begin{bmatrix}{\left( {{{1/1}/2013},230,450} \right),} \\{\left( {{{2/1}/2013},231,400} \right),} \\\ldots \\\left( {{{5/1}/2013},232,500} \right)\end{bmatrix}.$

Another array may be used with the traditional time series 500 to storemetadata associated with a date/time, such as whether the home price wasabove or below average for that time period, which may also be referredto as a “tick.” For example:

$\begin{bmatrix}{\left( {{{1/1}/2013},{``{above}"}} \right),} \\{\left( {{{2/1}/2013},{``{below}"}} \right),} \\\ldots \\\left( {{{5/1}/2013},{``{above}"}} \right)\end{bmatrix}.$

In situations where metadata regarding categories such as “above” avalue, “below” a value, on or after a certain date are accessed, aseparate look up needs to be performed before the metadata may beinterpreted. For example, knowing that the metadata associated with Jan.1, 2013 is “above” may tell a user that the specific home price on Jan.1, 2013 is above average. However, a separate lookup needs to beperformed to find out what the average home value is. Accessing detailsregarding the value associated with the traditional time series andaccessing metadata thus involves two separate steps accessing valuesstored in two separate data structures, which may be computationallycostly, especially if searching is involved.

Therefore, as shown in this example, in a traditional time series 500,finding the metadata associated with a numerical value may require alookup step or a mapping step that looks up a separate array. If thereare more than one metadata fields, such lookup step may becomputationally costly.

Moreover, in a traditional time series 500, it is also difficult tocreate a sample or slice of the time series that also includes themetadata. For example, metadata associated with the home price examplediscussed above may include state and/or city, population, averagehousehold income. Creating a sample or a slice of the traditional timeseries 500 would involve mapping steps that separately look up eacharray or other data structure that stores the metadata involving state,array or data structure that stores population, and so forth.

A traditional time series may also store metadata as a set of intervals.Each interval may contain a starting data, and ending date, and a statusduring the interval. For example, a time series may track a house'sprice over time. Each tick represents a date and an associated price forthe house. However, other metadata may also be tracked in associationwith that time series, such as the status of the loan associated withthat house over time (e.g. “loan up-to-date”, “loan payment behind 30days”, “loan payment behind 60 days”, etc.). A traditional time seriesmay not use an array of ticks to represent this status information.Instead, it may use a set of intervals. For example, one interval may be[Apr. 30, 2011, Dec. 29, 2011, “loan up-to-date”], and another may be[Dec. 30, 2011, Feb. 6, 2012, “loan payment behind 30 days], and yetanother may be [Feb. 7, 2012, Dec. 29, 2012, “loan up-to-date”].

One problem with representing statuses as intervals is that they cannotbe easily sliced or subsetted. For example, data in the data structureitself may need to be duplicated to split such intervals. If a timeseries is sliced on Jan. 1, 2012 as a split date, the interval needs tobe applied to both new time series, with the interval now split. Objecttime series does not have these limitations. In addition, intervals mayoverlap, whereas ticks in an object time series may not overlap whentracking metadata, making it easier to perform transformations andanalysis on the metadata.

FIG. 6 illustrates an example figure that may be generated and presentedto a user to visualize an Object Time Series 600 associated with aspecific instance of a payment event object. In this embodiment, theObject Time Series 600 includes a time series bar that displays a timeperiod between Oct. 1, 2012 and Jul. 1, 2013.

In this example, a Payment object type is associated with the ObjectTime Series. Due dates for the Payment object is set to the first day ofeach month. Each Payment object may include properties such astimeliness with values indicating whether a payment is on time, late, orin default. Each Payment object may also include properties such aspayment with values indicating whether a payment was successful orunsuccessful. In some instances, a payment may be unsuccessful because apayment check is bounced by a bank or there are not sufficient funds tocomplete a payment transaction. In some embodiments, all the propertiesof an object may be configured to be displayed or plotted. In some otherembodiments, some properties of an object may be configured to bedisplayed whereas some other properties of an object may be configurednot to be displayed.

In some embodiments, objects in an Object Time Series may be displayednext to the respective points in time. For example, in FIG. 6, dates onwhich payments are made are displayed. For example, on Oct. 1, 2012, apayment is made and it is on time. On Nov. 1, 2012, a payment is madeand it is also on time. On Dec. 1, 2012, an on-time payment is alsomade.

In some embodiments, certain objects may be configured to be not showndirectly in a visualization of an Object Time Series. For example, inObject Time Series 600, missed payments may be configured in such a waythat they are not displayed next to the due date, which may be the firstday of a month. In some other embodiments, missed payments may also bedisplayed.

On Jan. 1, 2013, a payment is missed. However, on Jan. 13, 2013, a latepayment is made, and it is successful. No payment is made on either Feb.1, 2013. On Mar. 1, 2013, there is also no payment. On Mar. 13, 2013, alate payment is made. However, this late payment is unsuccessful.Payment dates are also missed on April 1, May 1, and June 1^(st). ByJul. 1, 2013, the property value of the Payment object is set to defaultafter 5 consecutive months without a successful payment.

As shown in the example in FIG. 6, unlike traditional time series thatuse metadata to associate non-numerical values with respective points intime, any type of Object and property values may be directly associatedwith respective points in time in an Objective Time Series.

In some embodiments, information regarding intervals between two or morepoints in time may be easily identified by comparing the values of theobjects associated with an Object Time Series. For example, in FIG. 6,information regarding the interval between Dec. 1, 2012 and Jan. 13,2013 may be determined by comparing the values of the propertytimeliness. The value of the property timeliness of the Payment objectassociated with Dec. 1, 2012 is “on time.” The value of the propertytimeliness of the Payment object associated with Jan. 13, 2013 is“late”. Therefore, during the interval between Dec. 1, 2012 and Jan. 13,2013, payment timeliness has changed from “on time” to “late”.

In some embodiments, the values in each object associated with an ObjectTime Series may also contain other objects. For example, the Paymentobject involved in Object Time Series 600 may contain a property paymentsuccess, and this property may contain a value that is an object initself. For example, the value may contain a link object. For example,on Mar. 13, 2013, a late and unsuccessful payment is made. The value ofpayment unsuccessful may contain a link object, which links theunsuccessful payment to a detailed description of the failed paymenttransaction. In some embodiments, such information may be configured tobe displayed, while in other embodiments, such information may beconfigured in such a way that it is not displayed next to an Object TimeSeries.

FIG. 7 illustrates an example figure that may be generated and presentedto a user to visualize an Object Time Series 700 and the slicing of theobject time series. In the example of FIG. 7, two slicing functions areperformed on the Object Time Series 700.

The first slicing function is Slice (Q4 2012), which executescomputer-implemented instructions that automatically performs a slicingfunction that may slice the fourth quarter of the year 2012 from theObject Time Series 700. This may result in the generation of Object TimeSeries 720, which includes the three months in the fourth quarter of theyear 2012 and the objects associated with the three months.

In some embodiments, the generated Object Time Series 720 may be furthersliced. For example, a splicing function may be performed on the ObjectTime Series 720 to create another Object Time Series that has an equalor less duration of time as the Object Time Series 720.

In some embodiments, all the objects involved with the original ObjectTime Series 700 are kept in the generated Object Times Series 720. Insome other embodiments, choices of which objects, properties, and/orvalue types may be kept and/or excluded may be specified in a splicingfunction. For example, a user may specify that he or she does not wantto see whether payments are successful.

The second slicing function is Slice (Q2, 2013), which may executecomputer-implemented instructions that automatically performs a slicingfunction that may slice the second quarter of the year 2013 from theObject Time Series 700. This may result in the generation of Object TimeSeries 740, which includes the three months in the second quarter of theyear 2013 and the objects associated with the three months.

Although quarterly slicing is used as an example in FIG. 7, in someother embodiments, slicing functions may also be based on otherarbitrary time intervals. Slicing a traditional time series is difficultbecause metadata needs to be looked up separately for the arbitrary timeintervals, If there are multiple intervals, looking up metadata storedin a different data structure may involve several separate look ups andaccess operations. After a new Object Time Series is created by slicing,it may be saved, viewed, accessed, and recalled as any Object TimeSeries. It may also be sliced to create other Object Time Series.

FIG. 8 illustrates an example figure that may be generated and presentedto a user to visualize an Object Time Series 850 and the sampling of theObject Time Series 850.

In this example, a sampling function Sample (Quarterly) may executecomputer-implemented instructions that automatically perform a samplingfunction that may sample the Object Time Series on a quarter-to-quarterbasis. In some embodiments, a sampling function of an Object Time Seriesmay also allow a user to specify a starting point in time or an endingpoint in time of a sampling function. In some other embodiments, asampling function of an Object Time Series may use the beginning of theObject Time Series involved as the default starting point in time forthe sampling function.

In this particular example, the “Sample (Quarterly)” function samplesthe Object Time Series 850 on a quarterly basis (every three months). Inthis particular embodiment, sampling begins on Oct. 1, 2012. Therefore,the points in time that are sampled on a quarterly basis include: Oct.1, 2012, Jan. 1, 2012, Apr. 1, 2013, and Jul. 1, 2013.

In some embodiments, the sampling function may generate an Object TimeSeries 870 based on the sampled points in time and the objectsassociated with the points in time. In the example in FIG. 8, the ObjectTime Series 870 includes four points in time, Oct. 1, 2012, Jan. 1,2013, Apr. 1, 2013, and Jul. 1, 2013.

In some embodiments, the sampling an Object Time Series also includesall the objects associated with the original Object Time Series for eachsampled point in time. In this example, including all objects associatewith the original Object Time Series 850 would include the Paymentobject's properties such as timeliness and whether payment wassuccessful.

In some other embodiments, the sampling function may be configured toinclude a portion of the objects, properties, and/or values associatedwith the original Object Time Series. For example, a user may choose toexecute a sampling function that does not include the value in thePayment object indicating whether payments were successfully made.

Although quarterly sampling is used as an example in FIG. 8, in someother embodiments, sampling functions may also be based on otherarbitrary time intervals. This would be difficult and costly toimplement using a traditional time series because all the associatedmetadata needs to be separately looked up from individual arrays, or bysearching a set of intervals. After a new Object Time Series is createdby sampling, it may be saved, viewed, accessed as any Object TimeSeries. It may also be sampled to create other Object Time Series.

FIG. 9 illustrates an example figure that may be generated and presentedto a user to visualize an Object Time Series associated with multipletypes of objects and functions that may be used to access propertyvalues of the person object. In FIG. 9, Object Time Series 900 is shown.Object Time Series 900 may include multiple types of objects, such asPerson and Activity. Each object associated with Object Time Series 900may include one or more properties that may have one or more values.

In FIG. 9, the Object Time Series 900 includes multiple points in timebeginning from Feb. 1, 2013 and ending on Aug. 1, 2013. For example, onFeb. 1, 2013, a person named Karl K. is associated with the date. He wasreportedly to be in the city of Kyoto Japan on this date. He is reportedto have an occupation of consultant. The Object Time Series 900 does notinclude or does not have information regarding his phone number. TheObject Time Series 900 includes the activity that Karl K. is associatedwith as meetings.

Karl K. is again recorded in the Object Time Series 900 on Feb. 24,2013. On the particular day, he was reported to be in Guam. His phonenumber is also recorded in the Object Time Series 900.

On Apr. 23, 2013, a person named Frank J. is recorded in the Object TimeSeries 900 as being in the city of Moscow. His phone number is notrecorded. The activity associated with the date is a bank wire, and anaccount number is included. The date in this Object Time Series 900 isalso associated with a car, which is a blue Mini in this particularexample.

On Jun. 7, 2013, a person named Alex B. is recorded in the Object TimeSeries 900 as being in the city of Sao Paolo. Alex B. is recorded in theObject Time Series 900 as being seen with a person named Antonio B. andan unknown woman. Because more information about Antonio B. isavailable, the Object Time Series 900 may display the name Antonio B. asa link so that more information about Antonio B. may be found byfollowing the link. The activity involved on this date is recorded inthe Object Time Series 900 as equipment purchase and firearm purchase.

On Jul. 22, 2013, Frank J. is recorded in the Object Time Series 900 asbeing in the city of Kuala Lumpur. The activities he was associated withon this date include a bank wire and plane purchase.

In some embodiments, an Object Time Series such as Object Time Series900 may include multiple types of objects. For example, the Object TimeSeries 900 includes a Person object type, which may include propertiessuch as name, location, phone number, and so forth. The Object TimeSeries 900 may also include an Event or Activity object type, which mayinclude the activities recorded for a particular point in time, such asbank wires, purchases, meetings, and so forth.

Depending on embodiments, an Object Time Series may be created by usinga function such as object TimeSeries status=createObjectTime Series(“String”). As noted in FIG. 9, expressions such as 910: OTS.ValueOn(Apr. 25, 2013). Car may be used to access the car object associatedwith the date Apr. 25, 2013. In this particular example, the value ofthe car object type is a Blue Mini. An expression such as 920:OTS.ValueOn (Jul. 1, 2013). City may be used to access the city objectassociated with the date Jul. 1, 2013. Using an Object Time Series,information, values and properties that would be stored otherwise asmetadata as in traditional time series may be implemented and accessedeasily. Moreover, information, values and properties, especiallynon-numerical values, may be visually displayed and associated withrespective points in time. For example, for each person associated witha timeline, the person's picture, if available, may be displayed next tothe timeline. Other objects in an Object Time Series that may involveproperties and values that may be visually displayed may also beconfigured to be displayed as associated with respective points in time.

Depending on the embodiment, new object types may be introduced into anObject Time Series by applying filters, sampling, slicing, or otherfunctions or criteria on an Object Time Series. For example, in FIG. 9,the city object in the Object Time Series 900 may be used and associatedwith data/information related to weather in each city. Thus, a newobject type may be created that includes weather for each city. In someembodiments, a new Object Time Series may be created, which includesmapping between a plurality of points in time, the city objects/values,and weather objects/values for the cities. Additional objects may alsobe created based on the city object or any other object in an existingobject time series.

FIG. 9A illustrates an example figure that may be generated andpresented to a user to visualize two Object Time Series associated withstatus objects to show duration. As can be seen in the visualization 920of the two Object Time Series, the duration of a status may be easilydisplayed and presented to users using Object Time Series. In someembodiments, horizontal bars with various colors and/or patterns mayeasily show the transition from one status to another. The length ofeach status bar in 920 may also represent duration of a status. Forexample, Status 1 Object Time Series 921 has a duration of approximately180 days. The duration may be easily presented as the length of thehorizontal status bar.

FIG. 9B illustrates an example figure that may be generated andpresented to a user to visualize two Object Time Series associated withstatus objects indicating progress over time. As shown in this figure,in some embodiments, the visualization 930 of two Object Time Series maybe implemented as two lines. In this example, status 1 is represented asthe lowest status vertically. Status 3 is represented as the higheststatus vertically. Status 2 is in the middle between status 1 and 3.Object Time Series 931, for example, switched from status 2 to status 3around May 2012.

FIG. 9C illustrates an example figure that may be generated andpresented to a user to visualize two Object Time Series associated withvarious events. As shown in the visualization 940 of the two Object TimeSeries in FIG. 9C, the two Object Time Series are associated with 3event types each. The events may be displayed in the same visualizationby arranging event types related to the first Object Time Series on top,and event types related to the second Object Time Series below the firstObject Time Series. Using this visualization technique, events and eventtypes associated with the two Object Time Series may be compared to eachother directly.

FIG. 9D illustrates an example figure that may be generated andpresented to a user to visualize an Object Time Series associated with astatus object and a range of values associated with each status. In FIG.9D, the Object Time Series 950 involves statuses that are related tovarious amounts. For example, as shown in FIG. 9D, Status 3 isassociated with amounts ranging from 30K to approximately 150K. Using avisualization technique as shown in FIG. 9D, ranges of values associatedwith an object may also be displayed and associated directly with aplurality of points in time. In particular FIG. 9D represents one typeof visualization that combines the renderings for status (e.g. thebackground color), events (e.g. the event dots), and progress (e.g. theline graph portion).

FIG. 9E illustrates an example figure that may be generated andpresented to a user to visualize an Object Time Series associated with astatus object, ranges of values involved with each status, andtransitions among the various statuses. As shown in FIG. 9E, threedifferent statuses are involved in the Object Time Series 960 in thisexample. Status 1 is associated with list prices above $225K, and thetransition from status 1 to status 2 happened around October 2010.Status 2 is associated with list prices around $150K, and the transitionfrom status 2 and status 3 took place between November 2010 and May2011. Status 3 is associated with list prices above $25K but below $50K.Like with FIG. 9E, this figure represents one type of visualization thatcombines the renderings for status (e.g. the blue, green, and orangecolors), events (e.g. the event dots), and progress (e.g. the line graphportion).

Implementation Mechanisms

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, server computer systems, portable computersystems, handheld devices, networking devices or any other device orcombination of devices that incorporate hard-wired and/or program logicto implement the techniques.

Computing device(s) are generally controlled and coordinated byoperating system software, such as iOS, Android, Chrome OS, Windows XP,Windows Vista, Windows 7, Windows 8, Windows Server, Windows CE, Unix,Linux, SunOS, Solaris, iOS, Blackberry OS, VxWorks, or other compatibleoperating systems. In other embodiments, the computing device may becontrolled by a proprietary operating system. Conventional operatingsystems control and schedule computer processes for execution, performmemory management, provide file system, networking, I/O services, andprovide a user interface functionality, such as a graphical userinterface (“GUI”), among other things.

For example, FIG. 10 is a block diagram that illustrates a computersystem 800 upon which an embodiment may be implemented. Computer system800 includes a bus 802 or other communication mechanism forcommunicating information, and a hardware processor, or multipleprocessors, 804 coupled with bus 802 for processing information.Hardware processor(s) 804 may be, for example, one or more generalpurpose microprocessors.

Computer system 800 also includes a main memory 806, such as a randomaccess memory (RAM), cache and/or other dynamic storage devices, coupledto bus 802 for storing information and instructions to be executed byprocessor 804. Main memory 806 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 804. Such instructions, whenstored in storage media accessible to processor 804, render computersystem 800 into a special-purpose machine that is customized to performthe operations specified in the instructions.

Computer system 800 further includes a read only memory (ROM) 808 orother static storage device coupled to bus 802 for storing staticinformation and instructions for processor 804. A storage device 810,such as a magnetic disk, optical disk, or USB thumb drive (Flash drive),etc., is provided and coupled to bus 802 for storing information andinstructions.

Computer system 800 may be coupled via bus 802 to a display 812, such asa cathode ray tube (CRT) or LCD display (or touch screen), fordisplaying information to a computer user. An input device 814,including alphanumeric and other keys, is coupled to bus 802 forcommunicating information and command selections to processor 804.Another type of user input device is cursor control 816, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 804 and for controllingcursor movement on display 812. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Insome embodiments, the same direction information and command selectionsas cursor control may be implemented via receiving touches on a touchscreen without a cursor.

Computing system 800 may include a user interface module to implement aGUI that may be stored in a mass storage device as executable softwarecodes that are executed by the computing device(s). This and othermodules may include, by way of example, components, such as softwarecomponents, object-oriented software components, class components andtask components, processes, functions, attributes, procedures,subroutines, segments of program code, drivers, firmware, microcode,circuitry, data, databases, data structures, tables, arrays, andvariables.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Java, Lua, C or C++. A software modulemay be compiled and linked into an executable program, installed in adynamic link library, or may be written in an interpreted programminglanguage such as, for example, BASIC, Perl, or Python. It will beappreciated that software modules may be callable from other modules orfrom themselves, and/or may be invoked in response to detected events orinterrupts. Software modules configured for execution on computingdevices may be provided on a computer readable medium, such as a compactdisc, digital video disc, flash drive, magnetic disc, or any othertangible medium, or as a digital download (and may be originally storedin a compressed or installable format that requires installation,decompression or decryption prior to execution). Such software code maybe stored, partially or fully, on a memory device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules may be comprised of connectedlogic units, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Themodules or computing device functionality described herein arepreferably implemented as software modules, but may be represented inhardware or firmware. Generally, the modules described herein refer tological modules that may be combined with other modules or divided intosub-modules despite their physical organization or storage

Computer system 800 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 800 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 800 in response to processor(s) 804 executing one or moresequences of one or more instructions contained in main memory 806. Suchinstructions may be read into main memory 806 from another storagemedium, such as storage device 810. Execution of the sequences ofinstructions contained in main memory 806 causes processor(s) 804 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “non-transitory media,” and similar terms, as used hereinrefers to any media that store data and/or instructions that cause amachine to operate in a specific fashion. Such non-transitory media maycomprise non-volatile media and/or volatile media. Non-volatile mediaincludes, for example, optical or magnetic disks, such as storage device810. Volatile media includes dynamic memory, such as main memory 806.Common forms of non-transitory media include, for example, a floppydisk, a flexible disk, hard disk, solid state drive, magnetic tape, orany other magnetic data storage medium, a CD-ROM, any other optical datastorage medium, any physical medium with patterns of holes, a RAM, aPROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge, and networked versions of the same.

Non-transitory media is distinct from but may be used in conjunctionwith transmission media. Transmission media participates in transferringinformation between nontransitory media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 802. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 804 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 800 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 802. Bus 802 carries the data tomain memory 806, from which processor 804 retrieves and executes theinstructions. The instructions received by main memory 806 may retrievesand executes the instructions. The instructions received by main memory806 may optionally be stored on storage device 810 either before orafter execution by processor 804.

Computer system 800 also includes a communication interface 818 coupledto bus 802. Communication interface 818 provides a two-way datacommunication coupling to a network link 820 that is connected to alocal network 822. For example, communication interface 818 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 818 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN (or WAN component tocommunicated with a WAN). Wireless links may also be implemented. In anysuch implementation, communication interface 818 sends and receiveselectrical, electromagnetic or optical signals that carry digital datastreams representing various types of information.

Network link 820 typically provides data communication through one ormore networks to other data devices. For example, network link 820 mayprovide a connection through local network 822 to a host computer 824 orto data equipment operated by an Internet Service Provider (ISP) 826.ISP 826 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 828. Local network 822 and Internet 828 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 820and through communication interface 818, which carry the digital data toand from computer system 800, are example forms of transmission media.

Computer system 800 can send messages and receive data, includingprogram code, through the network(s), network link 820 and communicationinterface 818. In the Internet example, a server 830 might transmit arequested code for an application program through Internet 828, ISP 826,local network 822 and communication interface 818.

The received code may be executed by processor 804 as it is received,and/or stored in storage device 810, or other non-volatile storage forlater execution.

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The processes and algorithmsmay be implemented partially or wholly in application-specificcircuitry.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and subcombinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Any process descriptions, elements, or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved, as would be understood by those skilled in the art.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure. The foregoing description details certainembodiments of the invention. It will be appreciated, however, that nomatter how detailed the foregoing appears in text, the invention can bepracticed in many ways. As is also stated above, it should be noted thatthe use of particular terminology when describing certain features oraspects of the invention should not be taken to imply that theterminology is being re-defined herein to be restricted to including anyspecific characteristics of the features or aspects of the inventionwith which that terminology is associated. The scope of the inventionshould therefore be construed in accordance with the appended claims andany equivalents thereof.

What is claimed is:
 1. A computer implemented method comprising: undercontrol of a computing environment having one or more hardwareprocessors, the computing environment comprising one or more physicalcomputing systems configured to process large amounts of data,receiving, by the computing environment, data related to a plurality ofpoints in time; receiving, by the computing environment, input datarelated to one or more objects, wherein each object of the one or moreobjects comprises a data container for properties, and wherein eachproperty of the properties comprises one or more property typescorresponding to one or more property values, and wherein the one ormore objects are retrieved from a non-transitory computer-readablestorage medium; determining, from among the one or more objects, one ormore non-event objects based on the input data, wherein each non-eventobject of the one or more non-event objects comprises a property valuewhich has a non-numerical value; determining, from among the one or moreobjects, one or more event objects based at least in part on anassociation of the one or more event objects with the one or morenon-event objects; creating an object time series of a duration based onthe plurality of points in time, wherein each point in time of theplurality of points in time is associated with at least one object ofthe one or more non-event objects or the one or more event objects; andgenerating a visual representation of the object time series includingrepresentations of the one or more event objects with at least onerespective point in time, the one or more non-event objects with atleast one respective point in time, and at least one non-numerical valueassociated with the one or more non-event objects, and wherein thevisual representation of the object time series further comprises theobject time series as a bar or graph arranged generally horizontally orin landscape manner, with each object associated directly into the baror graph and associated with at least one respective point in time onthe object time series.
 2. The computer implemented method of claim 1,wherein the object time series is sampled based on a frequency, whereinthe frequency is at least one of weekly, bi-weekly, monthly, quarterly,or annually; and creating a second object time series using the sampledobject time series and objects associated with the sampled object timeseries.
 3. The computer implemented method of claim 1, wherein theobject time series is sliced based on a period of time that is of equalor shorter length of the duration of the object time series; andcreating a second object time series using the sliced object time seriesand objects associated with the sliced object time series.
 4. Thecomputer implemented method of claim 1, wherein the one or more objectsassociated with the object time series is accessed by a function callcomprising the name of the object time series and a direct request toaccess one or more non-numerical values associated with the one or moreobjects.
 5. The computer implemented method of claim 1, wherein the oneor more objects further comprise at least one other object, and whereinthe at least one other object comprises at least one property valuewhich has at least one non-numerical value.
 6. The computer implementedmethod of claim 5, wherein the at least one other object is visuallyrepresented in association with at least one respective point in time.7. The computer implemented method of claim 1, wherein the object timeseries is sampled based on an irregular time interval; and creating asecond object time series using the sampled object time series andobjects associated with the sampled object time series.
 8. A computersystem comprising: a computing environment, the computing environmentcomprising one or more physical computing systems configured to processlarge amounts of data; one or more computer processors; a tangiblestorage device storing a module configured for execution by the one ormore computer processors to: receive, by the computing environment, datarelated to a plurality of points in time; receive, by the computingenvironment, input data related to a one or more objects, wherein eachobject of the one or more objects comprises a data container forproperties, and wherein each property of the properties comprises one ormore property values, and wherein the one or more objects are retrievedfrom a second tangible storage device; determine, from among the one ormore objects, one or more non-event objects based on the input data,wherein each non-event object of the one or more non-event objectscomprises a property value which has a non-numerical value; determine,from among the one or more objects, one or more event objects based atleast in part on an association of the one or more event objects withthe one or more non-event objects; create an object time series of aduration based on the plurality of points in time, wherein each point intime of the plurality of points in time is associated with at least oneobject of the one or more non-event objects or the one or more eventobjects; and generate a visual representation of the object time seriesincluding representations of the one or more event objects with at leastone respective point in time, the one or more non-event objects with atleast one respective point in time, and at least one non-numerical valueassociated with the one or more non-event objects, and wherein thevisual representation of the object time series further comprises eachobject of the one or more event objects or the one or more non-eventobjects as visually connected with at least one respective point in timeon the object time series.
 9. The computer system of claim 8, whereinthe object time series is sampled based on a frequency, wherein thefrequency is at least one of weekly, bi-weekly, monthly, quarterly, orannually; and the one or more processors are configured to: create asecond object time series using the sampled object time series andobjects associated with the sampled object time series.
 10. The computersystem of claim 8, wherein the object time series is sliced based on aperiod of time that is of equal or shorter length of the duration of theobject time series; and the one or more processors are configured to:create a second object time series using the sliced object time seriesand objects associated with the sliced object time series.
 11. Thecomputer system of claim 8, wherein the one or more objects associatedwith the object time series is accessed by a function call comprisingthe name of the object time series and a direct request to access one ormore non-numerical values associated with the one or more objects. 12.The computer system of claim 8, wherein the one or more objects furthercomprise at least one other object, and wherein the at least one otherobject comprises at least one property value which has at least onenon-numerical value.
 13. The computer system of claim 12, wherein the atleast one other object is visually represented in association with atleast one respective point in time.
 14. The computer system of claim 8,wherein the object time series is sampled based on an irregular timeinterval; and the one or more processors are configured to: create asecond object time series using the sampled object time series andobjects associated with the sampled object time series.
 15. Anon-transitory computer-readable storage medium comprisingcomputer-executable instructions that direct a computing system to:under control of a computing environment, the computing environmentcomprising one or more physical computing systems configured to processlarge amounts of data, receive, by the computing environment, datarelated to a plurality of points in time; receive, by the computingenvironment, input data related to a one or more objects, wherein eachobject of the one or more objects comprises a data container forproperties, and wherein each property of the properties comprises one ormore property values, and wherein the one or more objects are retrievedfrom a second non-transitory computer-readable storage medium;determine, from among the one or more objects, one or more non-eventobjects based on the input data; determine, from among the one or moreobjects, one or more event objects based at least in part on anassociation of the one or more event objects with the one or morenon-event objects; create an object time series of a duration based onthe plurality of points in time, wherein each point in time of theplurality of points in time is associated with at least one object ofthe one or more non-event objects or the one or more event objects; andgenerate a visual representation of the object time series includingrepresentations of the one or more event objects with at least onerespective point in time, the one or more non-event objects with atleast one respective point in time, and at least one value associatedwith the one or more non-event objects, and wherein the visualrepresentation of the object time series further comprises each objectof the one or more event objects or the one or more non-event objectsvisually connected with at least one respective point in time on theobject time series.
 16. The non-transitory computer-readable storagemedium of claim 15, wherein the object time series is sampled based on afrequency, wherein the frequency is at least one of weekly, bi-weekly,monthly, quarterly, or annually; and the computing system is furtherdirected to: create a second object time series using the sampled objecttime series and objects associated with the sampled object time series.17. The non-transitory computer-readable storage medium of claim 15,wherein the object time series is sampled based on an irregular timeinterval; and the computing system is further directed to: creating asecond object time series using the sampled object time series andobjects associated with the sampled object time series.
 18. Thenon-transitory computer-readable storage medium of claim 15, wherein theobject time series is sliced based on a period of time that is of equalor shorter length of the duration of the object time series; and thecomputing system is further directed to: creating a second object timeseries using the sliced object time series and objects associated withthe sliced object time series.
 19. The non-transitory computer-readablestorage medium of claim 15, wherein the one or more objects associatedwith the object time series is accessed by a function call comprisingthe name of the object time series and a direct request to access one ormore non-numerical values associated with the one or more objects. 20.The non-transitory computer-readable storage medium of claim 15, whereinthe one or more objects further comprise at least one other object andwherein the at least one other object comprises at least one propertyvalue which has at least one non-numerical value.